Data-driven Web-based Intelligent Decision Support System for Infection
Management at Point-Of-Care: Case-Based Reasoning Benefits and
Limitations
Bernard Hernandez
1
, Pau Herrero
1
, Timothy M. Rawson
2
, Luke S. P. Moore
2
, Esmita Charani
2
,
Alison H. Holmes
2
and Pantelis Georgiou
1
1
Centre for Bio-Inspired Technology, Imperial College London, U.K.
2
Health Protection Unit in Healthcare Associated Infections and AMR, Imperial College London, U.K.
Keywords:
Antimicrobial Resistance, Infection Diseases, Antibiotics, Decision Support System, Case-Based Reasoning,
Machine Learning, User Interface, Point of Care.
Abstract:
Antimicrobial Resistance (AMR) is a major patient safety issue. Attempts have been made to palliate its
growth. Misuse of antibiotics to treat human infections is a main concern and therefore prescription behaviour
needs to be studied and modified appropriately. A common approach relies on designing software tools to
improve data visualization, promote knowledge transfer and provide decision-making support. This paper
explains the design of a Decision Support System (DSS) for clinical environments to provide personalized,
accurate and effective diagnostics at point-of-care (POC), improving continuity, interpersonal communication,
education and knowledge transfer. Demographics, biochemical and susceptibility laboratory tests and individ-
ualized diagnostic/therapeutic advice are presented to clinicians in a handheld device. Case-Based Reasoning
(CBR) is used as main reasoning engine to decision support for infection management at POC. A web-based
CBR-inspired interface design focused on usability principles has also been developed. The proposed DSS is
perceived as useful for patient monitoring and outcome review at POC by expert clinicians. The DSS was rated
with a System Usability Scale (SUS) score of 68.5 which indicates good usability. Furthermore, three areas of
improvement were identified from the feedback provided by clinicians: thorough guidance requirements for
junior clinicians, reduction in time consumption and integration with prescription workflow.
1 INTRODUCTION
Antimicrobials are drugs that kill or stop the growth
of microbes (e.g. bacteria or viruses), thereby are
commonly used to treat infections. Recently, An-
timicrobial Resistance (AMR) has been reported to
be a leading public health and safety problem (Wise
et al., 1998; ONeill, 2014) with the inappropriate
use of antibiotics in humans identified as a leading
driver (Holmes et al., 2016). Microbes are contin-
uously evolving and unnecessary antibiotic prescrip-
tion, particularly within infection diseases, are a com-
mon concern in critical care and infection manage-
ment, which are observing and suffering the conse-
quences of an increased rate of AMR. In addition,
failure to recognize and respond to the early stage in-
fections is considered a major cause of avoidable mor-
tality. Thus, it is needed to develop guidelines and
software tools that facilitate healthcare professionals
to treat their patients at the patient bedside by collect-
ing and visualizing laboratory test results while pro-
viding a support system to assist in decision-making.
Antibiotic resistance is most likely to develop in
areas with a considerable concentration of sick pa-
tients and high risk of infection where antimicrobials
are used extensively. Henceforth, the Intensive Care
Unit (ICU), where proportion of inappropriate antibi-
otic prescription ranges from 41% to 66%, is targeted
in our preliminary studies. Handheld Decision Sup-
port Systems (DSSs) including local antibiotic guide-
lines have proved to reduce antibiotic prescribing in
the ICU (Sintchenko et al., 2005). Despite their ben-
efits, factors as hardware availability or interface de-
sign (Tsopra et al., 2014) obstruct the acceptance of
DSSs in clinical environments. To promote their use,
it is necessary to determine the best way to present
the information (Moxey et al., 2010). The Health-
care Information and Management Systems Society
(HIMMS) stressed the benefits of designs based on
usability principles (Belden et al., 2009). In addi-
Hernandez B., Herrero P., Rawson T., Moore L., Charani E., Holmes A. and Georgiou P.
Data-driven Web-based Intelligent Decision Support System for Infection Management at Point-Of-Care: Case-Based Reasoning Benefits and Limitations.
DOI: 10.5220/0006148401190127
In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 119-127
ISBN: 978-989-758-213-4
Copyright
c
2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
119
tion, a well designed DSS sustains the advanced al-
gorithms implemented in its core with a fully com-
prehensible representation to support confidence in
doctors. Since medical knowledge is voluminous, it
has to be focused on data and decision making while
providing access to electronic health records (HER)
and personal health information (PHI). Many stud-
ies show that young clinicians engage better with the
use of mobile applications, displaying great potential
to improve learning and augmenting traditional train-
ing (Boulos et al., 2014).
In this paper we postulated that an appropri-
ately designed clinical information technology sys-
tem could improve reliability and consistency of col-
lecting vital signs; their visualization, interpretation
and analysis; and the delivery of a more sophisti-
cated DSS. Therefore, health care professionals and
biomedical engineers from Imperial College of Lon-
don have designed a prototype system accessible at
the point-of-care (POC) with the specific objectives
of improving three main areas: personalization and
therefore outcomes of infection management; conti-
nuity through POC support for interpersonal commu-
nication; and education during interactions between
clinicians and infection specialists.
2 BACKGROUND
Critical care, infection management and antimicrobial
stewardship is predominantly multidisciplinary with
involvement of infection specialists being crucial. In
practice, antimicrobial prescribing frequently occurs
out of hours, and when advice is dispensed by in-
fection specialists, uptake can be variable. Current
patient management systems rarely integrate DSSs to
assist with this, or if they do, this is very basic. There-
fore, there is an evident need for an intelligent clinical
DSS.
2.1 Decision Support Systems
A clinical DSS can be defined as a computer program
that is designed to analyse data to help health care
professionals make clinical decisions. They are meant
to increase quality of care, enhance health outcomes
and reduce human errors while improving efficiency,
cost-benefit ratio and patient satisfaction. Most ba-
sic systems include assessment, monitoring and in-
formative tools in the form of computerized alerts,
reminders and electronic clinical guidelines. For ex-
ample, therapy and vital signs monitoring (McGregor
et al., 2006) or susceptibility test results visualiza-
tion (Flottorp et al., 2002). More advanced diagno-
sis and advisory tools usually rely in statistics, ma-
chine learning and artificial intelligent techniques to
provide a higher level of data extraction. For exam-
ple, diagnose and therapy advisers (Paul et al., 2006)
or infection risk assessment (Mullett et al., 2004).
Different approaches have been used to design
intelligent DSS, each one with their own benefits
and drawbacks. Decision trees are popular for their
simplicity to understand and construct from logi-
cal rules and have been applied in dengue fever di-
agnosis (Tanner et al., 2008) and antibiotic selec-
tion (William Miller, 2013). The amount of comput-
ing time required for large datasets is still reasonable.
However, they do not tend to work well if decision
boundaries are smooth (Quinlan, 1986) and are not
optimal for uncorrelated variables. As a result of the
greedy strategy applied, they also present high vari-
ance and are often unstable, tending to over-fit.
Probability-based approaches are emerging due
to its capacity to represent and handle uncertain-
ties (Pearl, 1988). Bayesian Networks (BN) are prob-
abilistic networks that represent a set of variables
(nodes) and their dependencies (arcs) using a graph.
Such causal dependencies, influences or correlations
are defined based on the experience of clinicians.
Hence, it can be associated with a rule-based sys-
tem, which uses data to refine the previously defined
relationships. They have been widely exploited in
health-care (Lucas et al., 2004). Particularly, Causal
Probabilistic Networks (CPN) have been used to de-
velop DSS in diagnosis of cancer (Kahn et al., 1997),
ventilator-associated pneumonia (Lucas et al., 2003)
and sepsis (Tsoukalas et al., 2015). Bayesian Net-
works offer a natural way of representing uncertain-
ties, however an insufficient understanding of their
formal meaning may give rise to modelling flaws.
In particular, Causal Probabilistic Networks are best
suited to tackle very specific situations as bloodstream
infection (Paul et al., 2006). Unfortunately, treatment
recommendation is poor since they usually prescribe
broad-spectrum antibiotics (Kofoed et al., 2009). Fur-
thermore, there is a lack of guidance to report and in-
terpret their results by non experts.
The Case-Based Reasoning (CBR) methodol-
ogy (Aamodt and Plaza, 1994) has been used to tackle
problems in antibiotic therapy (Heindl et al., 1997)
and molecular biology (Jurisica and Glasgow, 2004).
The aim is to use previous experience in form of cases
to understand and solve new problems.
2.2 Case-Based Reasoning
Case-based reasoning is a widely used approach to
solve new problems based on previous experience in
HEALTHINF 2017 - 10th International Conference on Health Informatics
120
form of cases. It is considered as a methodology to
follow rather than an algorithm in itself as shown in
Figure 1. The CBR cycle is divided in four different
phases. The first phase retrieves from the database
those cases that are relevant based on a predefined
similarity measure (e.g. euclidean distance). In the
second phase, advice is commonly given by adapting
or combining the solutions from the retrieved cases
(i.e. antibiotic therapies). The proposed solution is
incorporated to the case and saved in the database.
The third phase monitors the treatment evolution to
assess its outcome (e.g. success or failure). Finally, a
decision to whether retain or not the case based on its
reusability is made.
Solved
Case
Retrieved
Similar
Cases
Current
Case
Learned
Case
Tested/
Repaired
Case
Case i
R
E
U
S
E
R
E
T
R
I
E
V
E
R
E
T
A
I
N
R
E
V
I
S
E
Case Base
General Knowledge
Problem
Figure 1: Diagram showing the different phases for a cycle
within the Case-Based Reasoning methodology as outlined
in (Aamodt and Plaza, 1994).
This methodology is very generic and can be par-
ticularized to tackle many different problems. Nev-
ertheless, the most important property that makes
CBR appropriate to be used in clinical environments
is the straightforward relation that can be found be-
tween cases in the CBR methodology and cases as
interpreted by clinical staff. Due to this nexus be-
tween the clinical and the scientific environments,
CBR methodology has been selected to be incorpo-
rated in the decision support system and strongly in-
fluenced the design of the user interface.
3 METHODOLOGY
3.1 EPIC IMPOC
Enhanced, Personalized and Integrated Care for In-
fection Management at Point Of Care (EPIC IMPOC)
is a decision support system designed to record a com-
plete set of vital signs at the patients bedside on hand-
held computing devices while providing instant bed-
side decision-making assistance to clinical staff. It
also pulls in data from the hospital patient admin-
istration system, laboratory results and other clini-
cal information stored electronically. It can be used
anywhere in the hospital by staff with appropriate ac-
cess rights, using mobile devices or desktop comput-
ers linked to the hospital intranet. EPIC IMPOC has
been preliminarily trialled at critical care antimicro-
bial prescribing, a known reservoir for antimicrobial
resistance, and it is being extended to secondary care.
The system architecture is shown in Figure 2 were
two parts are clearly differentiated: server and client
sides. The server side processes queries, interacts
with the permanent storage and serves web pages
to the client side. The latter displays information
to users. The modules constituting the server side
are: a) CBR for history review and case compari-
son. b) Probabilistic Inference (PI) aims to provide
step-wise guidance fitting the decision pathway fol-
lowed by clinicians for infection management. c) Pa-
tient engagement module. d) Personalized antibiotic
dosing. e) Visualization of Antimicrobial Resistance
related information. This paper focuses exclusively
on the CBR module.
3.2 Server Side
The server side has been implemented in Java and
uses an object-relational mapping java library (Hi-
bernate ORM) to map an object-oriented domain
model to a traditional relational database (SQL). The
Lightweight Directory Access Protocol (LDAP) ac-
complishes the authorization and authentication of
users and it is provided in all hospitals at Imperial
College Healthcare National Health Service Trust.
The server side follows the REST (Representational
State Transfer) architectural design, which suggests a
group of guidelines to create scalable, more perfor-
mant and maintainable web services.
The core CBR module implementation is based on
the JColibri framework (D
´
ıaz-Agudo et al., 2007) in-
cluding some improvements to achieve better gener-
alization and performance. It is used to retrieve cases
from the database based on a similarity measure. A
case is defined by a compendium of parameters that
Data-driven Web-based Intelligent Decision Support System for Infection Management at Point-Of-Care: Case-Based Reasoning Benefits
and Limitations
121
EPIC IMPOC
Dosing
PI
CBR
AMR
Trends
Antibiogram
Patient
Module
API
ADM IN
P AT H
M ICRO
LAB.RESU LT S
HIST ORY
ST AT S
Server
Client
Figure 2: High-level diagram describing the main components of the DSS. The external databases that are currently being ac-
cessed are patient administration system (ADMIN), pathology laboratory tests (PATHO) and microbiology results (MICRO).
The server side has the following independent modules: Case-Based Reasoning (CBR), Probabilistic Inference (PI), Patient
module, Dosing module, antibiogram and AMR trends. All the information is accessed through an API and presented on a
handheld device to clinicians.
can be grouped in five different sets: metadata, de-
scription, solution, justification and result. However,
only those in the description container are used to
compute the similarity scores. Some examples of
attributes used to define the case are: demograph-
ics (age, gender or weight), existing diseases (aller-
gies, HIV or diabetes), respiratory system (ventilation
support or oxygen requirements), abdomen (abdomi-
nal examination, renal support or catheter), biochemi-
cal markers (creatinine or bilirubin) and microbiology
(infectious organisms).
3.3 Client Side
The client side is a web-based application imple-
mented using HTML, CSS and Javascript which is
accessible through the browser. It follows a respon-
sive design approach to render a consistent interface
across different devices, from desktop computers to
mobile phones and tablets with different screen sizes.
An efficient DSS user interface should present all
the information relevant to clinicians neatly, combin-
ing different resources of patient-related information
(i.e. demographic, pathology and microbiology data).
Since some data might be missing or not available, it
is also desired to enable clinicians to manually input
data or comments for further consideration. In addi-
tion, infections evolve with time and so do treatments.
Thus, inspection of previous symptoms, treatments
and outcomes is desired. Since there is an straightfor-
ward relation between cases as interpreted in clinical
environments and cases in the CBR methodology, a
case is considered as main unit of patient related in-
formation to be presented in the interface. A single
case is formed by several components, mostly regard-
ing the type and source of the data, and has been di-
vided in different sections (tabs in Figure 3) for visu-
alization purposes. The Resume section is read-only
and displays the most relevant information (e.g. in-
fectious micro-organisms or organs infected) while
Description shows additional information and allows
the insertion/modification of parameters within the
case. Solution contains the antibiotic therapy pre-
scribed (including frequency, via and doses) and a
section to collect feedback from users.
Six routinely requested biochemical markers were
selected as main indicators of infection and patient
status after reviewing the scientific literature and dis-
cussion with clinicians and infectious disease experts.
The temporal evolution of such biochemical markers
is shown in Pathology (see Figure 3) as time-series
where coloured background indicates the normal ref-
erence range. Additionally, it is possible to hide/show
time-series to improve visualization.
Susceptibility testing is used to determine which
antimicrobials will inhibit the growth of micro-
organisms causing a infection and is generally per-
formed in vitro. The Microbiology section displays
the result of all the susceptibility tests requested for
the patient. The outcomes of the tests are provided
for individual pairs (infectious organism and antibi-
otic) and are categorised as resistant, intermediate and
sensitive. They are also presented during antibiotic
therapy selection for further guidance.
HEALTHINF 2017 - 10th International Conference on Health Informatics
122
Figure 3: EPIC IMPOC web-based decision support system overview. The main unit of information is the case and its
content is displayed among ve different tabs (Resume, Description, Pathology, Sensitivity and Solution). The user interface
is divided in three main areas: patient selection, dashboard with current patient (top) and retrieved cases (bottom), history
review and a side bar to add/remove cases to/from such dashboard.
4 RESULTS
A working prototype of EPIC IMPOC incorporat-
ing Case-Based Reasoning methodology as decision-
support engine was preliminarily trialled in the In-
tensive Care Unit at Hammersmith Hospital in Lon-
don for a month. The predefined case base contained
80 cases and information retrieval was performed
through handheld computer devices (i.e. ipads) at the
patient bed side by clinicians under the supervision of
infection specialists. From such study the following
conclusions were extracted:
The system has potential to promote and facil-
itate communication between nurses, clinicians
and infection specialists as shown by the interac-
tion among them during the trial.
The system improves homogeneous collection of
vital signs. Such improvement comes from the
introduction of a form in the description of the
case to input missing symptoms easily. The form
is filled automatically for those symptoms avail-
able in external databases (e.g. electronic health
records).
The system facilitates data visualization at POC
and simplifies comparison with previous similar
cases and outcomes. In addition, biochemical
markers evolution, susceptibility tests and history
review for the hospitalized patient are easily ac-
cessible and found to be very helpful at point of
care.
The system is capable of mimicking clinicians
prescription practices in the intensive care unit. In
such trial clinicians were under the supervision of
infection specialists. As a result, therapies pre-
scribed by clinicians and therapies retrieved by
the CBR algorithm matched approximately 90%
of the times. It is especially visible in ICU where
wide-spectrum antibiotics are commonly used.
It increases and facilitates the interaction between
clinicians and patients. Therefore it helps engag-
ing with patients and opens the possibility to ed-
ucate population on antibiotic misuse and its con-
sequences (Rawson et al., 2016b).
4.1 System Usability Scale Survey
A survey to evaluate the usability of the decision sup-
port system interface was performed. The System Us-
ability Scale (SUS) (Brooke et al., 1996) is composed
Data-driven Web-based Intelligent Decision Support System for Infection Management at Point-Of-Care: Case-Based Reasoning Benefits
and Limitations
123
Table 1: The original SUS statements (Brooke et al., 1996), average agreement and SUS contribution.
SUS statements Avg. rating SUS contribution
I think that I would like to use this system frequently. 2.8 1.8
I found the system unnecessarily complex. 1.4 3.6
I thought the system was easy to use. 2.0 1.0
I think that I would need the support of a technical person to be able to use this system. 1.6 3.4
I found that the various functions in this system were well integrated. 3.0 2.0
I thought that there was too much inconsistency in this system. 0.2 4.8
I would imagine that most people would learn to use this system very quickly. 2.8 1.8
I found the system very cumbersome to use. 2.2 2.8
I felt very confident using the system. 3.0 2.0
I needed to learn a lot of things before I could get going with this system. 0.8 4.2
of 10 statements to which participants indicate their
agreement from 1 to 5, where 5 indicates strongly
agree. Predefined rules for positive and negative state-
ments are used to obtain the SUS contributions. The
SUS contribution for each statement ranges from 1
to 4 where higher scores indicate better usability (see
Table 1) and their sum is multiplied by 2.5 to calcu-
late the final SUS score. It ranges from 0 to 100 where
poor and great product usability are indicated for SUS
scores under 50 and over 80 respectively. This survey
is technology agnostic, quick, provides a single score
and is non proprietary. A free-text box was added for
additional comments and suggestions.
The SUS survey was completed by 10 different
participants (83% males) from 27 to 51 years old
where technical training in the use of the system was
not provided. The profile of those participants was in-
fection specialist (two), clinician (three), nurse (four)
and other staff (one). The SUS contribution for each
statement is presented in the right column in Table 1.
The final SUS score obtained is 68.5 which indicates
good product usability with margin to improve. Ad-
ditionally, a variety of comments were provided by
participants and have been synthesized in the follow-
ing bullet points:
There is a common concern among experienced
clinicians and infection specialists in the use ju-
nior doctors would do of such large amount of
data displayed in the interface. The decision sup-
port system has potential to help training junior
doctors and improve their prescription practices,
but it needs to narrow the presented information
providing specific guidance.
Clinicians consider the user interface intuitive and
helpful for patient long-term monitoring and man-
agement, however it might sometimes be time
consuming. Additionally, it does not entirely fit
with the work-flow followed to prescribe antibi-
otic therapies.
They suggested the possibility of recording fur-
ther parameters, not necessarily directly related
with infections.
From the preliminary trial performed by infection
specialists and the feedback obtained from the sur-
veys, it is possible to conclude that the CBR algorithm
is able to mimic the prescription practices of users.
However, that is not enough to promote change in an-
tibiotic prescription practices. Initially, as a quick so-
lution infection specialists were keen in creating an
“ideal” case base; that is, a set of cases with opti-
mal antibiotic therapies according to infection guide-
lines and expert prescriptions. Such optimal therapies
would then be suggested by the decision support sys-
tem to further users. Hence, the knowledge would be
transferred from infection specialists to other clinical
staff (e.g. nurses and clinicians).
Unfortunately, this approach presents several
drawbacks. Creating a complete case base that covers
the whole spectrum of possibilities is nearly impossi-
ble and time consuming. In addition, infections are
often acquired in hospitals by contagious as a con-
sequence of treating more severe diseases which di-
minish the immune system (e.g. surgeries and can-
cer). Therefore, future therapies prescribed by clin-
icians and recorded in the system might not agree
with the infection guidelines reshaping the case base
and therefore altering CBR recommendations. A case
base with strictly guideline oriented therapies on the
long-term is unrealistic and limits the scope and us-
ability of the system.
After discussion with a multi-professional team
including physicians, nurses, pharmacists and non-
medical researchers, an study to map the pathway
followed by clinicians to prescribe antibiotics thera-
pies was performed (Rawson et al., 2016a). The re-
ported infection management pathway was defined as
a stepwise Bayesian model of estimating probabili-
ties in which each step adds systematically informa-
tion to allow optimisation of decisions on diagnosis
and management of infection. Initially, clinicians es-
timate the risk of infection and attempt to localize its
HEALTHINF 2017 - 10th International Conference on Health Informatics
124
source by looking at patient’s physiological parame-
ters. Once clinicians construct a picture of the severity
of the infection, whether or not to initiate antimicro-
bial therapy is decided. In this step, local microbiol-
ogy guidance provided within hospitals was the most
commonly cited factor. Finally, they review and refine
the treatment accordingly.
This new approach enables to produce very spe-
cific step-by-step probability-like decision support.
This would improve the guidance provided to junior
clinicians and facilitates the validation of decision-
making for each individual step. Additionally, since
reviewing of previous cases is not necessary it would
greatly reduce usability time. Since the methodology
integrates with the infection management pathway, it
will likely influence prescription practices to a higher
degree than CBR.
5 DISCUSSION
DSSs are being exploited in several areas such as
business or economics but their acceptance by clin-
ical staff is obstructing its use in hospitals and other
clinical environments (Kawamoto et al., 2005). De-
signing a DSS based on usability principles and sim-
ply providing the clinical information does not guar-
antee acceptance (Moxey et al., 2010). Other factors
as accessibility, availability, easy of use, time require-
ments and integration into the clinical workflow are
important and need to be considered (Tsopra et al.,
2014). Taking previous knowledge in consideration,
a DSS to support prescription of antibiotic therapies
and patient monitoring at POC exploiting the CBR
methodology was implemented.
In many circumstances, as complicated cases,
providers prefer to consult their colleges or more
specialised clinicians as infection specialists. This
consultation among different members of the clini-
cal staff was facilitated by the DSS. In addition, it
was believed to enhance decision making and homo-
geneous collection of vital signs among clinicians re-
sulting in better prescribing practices. Furthermore,
re-entering patient data to generate advices is a deter-
rent to use (Moxey et al., 2010) and integration into
existing programs (e.g. electronic medical records)
was a clear facilitator.
The usability measured through the SUS survey
was 68.5 which is about average and shows poten-
tial margin for improvement. A similar strength of
agreement was shown by participants for the third and
eight statements which had an average rating of 2.0
and 2.2 respectively. The SUS contribution for each
statement was 1.0 and 2.8 respectively, indicating that
the system is usable but not necessarily easy. Note
that some wording used by the original SUS was sug-
gested to be poorly understood by participants affect-
ing the results (Bangor et al., 2008). As an exam-
ple, the sixth statement which contributed the most
contains the wording “too much” which might be un-
clear. Additionally, users may not have a good sense
of the required complexity of such systems since there
are no commonly known competing solutions and
the wording “unnecessarily” in the second statement
might have led to a higher contribution of 3.6. There-
fore, the final SUS score is possibly slightly less that
the one presented.
There is still much to be done to make these sys-
tem work in routine clinical practice. Measuring ab-
solute usability using a single metric is very challeng-
ing since many external factors influence the results
(e.g. technical training or accessible technology). The
feedback provided greatly helped to identify areas of
improvement and the results of the survey are a use-
ful source for future comparison to asses the benefits
of including new components to tackle the identified
weaknesses.
6 CONCLUSIONS
CBR methodology was incorporated in a DSS as
decision-making engine providing similar antibiotic
therapies to those prescribed by expert clinicians in
the majority of cases in our preliminary trials. In
addition, it was widely accepted for information re-
trieval and long-term patient monitoring and manage-
ment. Three main areas of improvement were identi-
fied from the feedback provided by expert clinicians:
specific guidance requirements for junior clinicians, a
need to reduce time consumption using the DSS and
a better integration into clinical workflow.
The reported infection management pathway was
defined as a multi-step Bayesian-like approach (Raw-
son et al., 2016a) which inherently tackles most of the
weaknesses identified: specific guidance, time con-
straints and integration in the workflow. Therefore,
it is more suitable to support and modify prescription
practices.
The combination of both approaches into one sin-
gle decision support system is a very elegant solution
that could lead to an increase in acceptability among
clinicians. To validate its feasibility, infection risk and
source of infection inference from biochemical mark-
ers are considered as primary steps. The final integra-
tion of such inference in the user interface has poten-
tial to reduce misuse of antibiotics and its evaluation
forms the basis for future work.
Data-driven Web-based Intelligent Decision Support System for Infection Management at Point-Of-Care: Case-Based Reasoning Benefits
and Limitations
125
ACKNOWLEDGEMENT
This report is independent research funded by the
National Institute for Health Research Invention for
Innovation (i4i) programme, Enhanced, Personalized
and Integrated Care for Infection Management at
Point of Care (EPIC IMPOC), II-LA-0214-20008.
The authors would like to thank members of Impe-
rial College NHS Healthcare Trust who participated
in the study. The views expressed in this publication
are those of the authors and not necessarily those of
the NHS, the National Institute for Health Research
or the UK Department of Health.
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